Characterization of Hidden Paint Layer Topography Using a Stereographic XRF Approach

Master Thesis (2017)
Author(s)

J.R. Allred (TU Delft - Mechanical Engineering)

Contributor(s)

Joris Dik – Mentor

Faculty
Mechanical Engineering
Copyright
© 2017 Jennie Allred
More Info
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Publication Year
2017
Language
English
Copyright
© 2017 Jennie Allred
Graduation Date
21-12-2017
Awarding Institution
Delft University of Technology
Programme
['Materials Science and Engineering']
Faculty
Mechanical Engineering
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Abstract


Scientific investigation of paintings has been facilitated by the development of advanced non-de\-struc\-tive imaging methods. Characterization of painting stratigraphy traditionally requires extraction of small paint samples, thereby limiting its use to a few locations on a painting due to its destructive nature. Alternatively, non-destructive analysis of paint layer stratigraphy and structure across an entire painting often requires highly specialized and costly equipment, and/or the transport of priceless artworks. In addition, most methods are also typically limited to a localized point analysis.

This document proposes an alternative method for the substructure examination of paintings using a mobile macro-XRF spectrometer and a stereographic approach with reduced step sizes. This is coupled with a novel data analysis method which will enable a global study of the topographical features of hidden paint layers. As a prototype to test the feasibility of the method, we utilized a two-layer test sample consisting of pastose bone black pigment on pastose lead white, with an aluminum substrate. High resolution 3D optical microscopy was utilized to establish a ground truth for the thicknesses of both paint layers.

Through successful registration of MA-XRF scans obtained with varying detector geometries into a single hyrbid image, we were able to find a strong correlation between the quanitative height data obtained with optical microscopy and our hybrid XRF image. Our findings indicate that utilizing this approach for visualizing hidden paint layer topographies proves to be very promising. Coupled with novel data fusion algorithms and visualization techniques, additional insight about painterly technique can thus be gained by utilizing existing MA-XRF scanners to scan a painting in multiple orientations.

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